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Relevant bibliographies by topics / Large language model

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Academic literature on the topic 'Large language model'

Author: Grafiati

Published: 29 July 2024

Last updated: 25 July 2025

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Journal articles on the topic "Large language model"

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Kumar, Deepak, Dr Amandeep, Pinki Pinki, et al. "Visualization by Natural Language Processing and Large Language Model." International Journal of Research Publication and Reviews 6, no. 6 (2025): 11225–31. https://doi.org/10.55248/gengpi.6.0625.2340.

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Awotunde, Opeyemi Joseph. "Continuous Model Calibration: Leveraging Feedback-Driven Fine-Tuning for Self- Correcting Large Language Models." International Journal of Research Publication and Reviews 6, no. 3 (2025): 4145–58. https://doi.org/10.55248/gengpi.6.0325.1208.

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Lorenz, Kilian, Pascal Bürklin, Klemens Schnattinger, et al. "Refinetuning Decentralized Large Language Model for Privacy-Sensitive University Data." Journal of Robotics and Automation Research 6, no. 2 (2025): 01–11. https://doi.org/10.33140/jrar.06.02.01.

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This work focuses on refining a decentralized large language model (LLM) tailored for finetuning on privacy-sensitive university data. Devolved AI models, designed to operate across multiple distributed nodes, offer a promising solution for handling sensitive information by ensuring data remains localized at its source while collaboratively training a global model. The key challenge addressed in this study is the adaptation and fine-tuning of a decentralized LLM to work effectively with heterogeneous, privacyrestricted datasets typical in university environments, such as student records, resea

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B, Mr DHANUSH. "CHATBOT USING LARGE LANGUAGE MODEL." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34001.

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The concept of Natural Language Processing has seen a remarkable advancement in the recent years. This remarkable advancement was particularly with the development of Large Language Models (LLM). Large Language Models are used to develop a human like conversations. This LLM is a part of Natural Language Processing which focuses on enabling computers to understand, interpret, and generate human language. The existing system of chatbots does not generate human like responses. The proposed system of chatbots uses the power of Large Language Models to generate more human like responses, providing

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Zhang, Chengyi, Xingyu Wang, and Ziyun Wang. "Large language model in electrocatalysis." Chinese Journal of Catalysis 59 (April 2024): 7–14. http://dx.doi.org/10.1016/s1872-2067(23)64612-1.

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Salem, Nadia, Khawla Al-Tarawneh, Amjad Hudaib, et al. "Generating database schema from requirement specification based on natural language processing and large language model." Computer Research and Modeling 16, no. 7 (2024): 1703–13. https://doi.org/10.20537/2076-7633-2024-16-7-1703-1713.

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Sagi, Sriram. "Advancing AI: Enhancing Large Language Model Performance through GPU Optimization Techniques." International Journal of Science and Research (IJSR) 13, no. 3 (2024): 630–33. http://dx.doi.org/10.21275/sr24309100709.

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Lee, Youn-Kyoung. "Integrating the Large Language Model into L2 Pronunciation Teaching and Learning." Studies in Modern Grammar 125 (March 31, 2025): 129–46. https://doi.org/10.14342/smog.2025.125.129.

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Baral, Elina, and Sagar Shrestha. "Large Vocabulary Continuous Speech Recognition for Nepali Language." International Journal of Signal Processing Systems 8, no. 4 (2020): 68–73. http://dx.doi.org/10.18178/ijsps.8.4.68-73.

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Speech Recognition is a widely studied topic for high-resource languages like English and Mandarin. A plethora of publications exist that study the performance of several recognition methods for these languages. However differences in phonetics, accent, language model, etc between any two different languages demand for a study of speech recognition methodologies and components separately for each language. In this paper, we present a comparative study of popular speech recognition methods for Nepali, a low-resource Indo-Aryan language. We describe our approach to building the phonetic dictiona

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Lu, Zhouquan. "Large Language Models: Development in Model Scale and Challenges." Applied and Computational Engineering 114, no. 1 (2024): 154–61. https://doi.org/10.54254/2755-2721/2024.18276.

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Since the 1950s, language modeling (Language Models, LMs) has been one of the primary approaches for tasks such as machine translation, as well as language understanding and processing. It has been widely applied in the field of natural language processing (Natural Language Processing, NLP), significantly improving the performance of tasks related to natural language understanding and generation. In recent years, large language models (LLMs) have made remarkable advancements in technical architecture and model scale, providing strong technological support for NLP and other fields. This paper p

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Dissertations / Theses on the topic "Large language model"

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Jiang, Yuandong. "Large Scale Distributed Semantic N-gram Language Model." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1316200173.

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Tang, Haijiang. "Building phrase based language model from large corpus /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202002%20TANG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.<br>Includes bibliographical references (leaves 74-79). Also available in electronic version. Access restricted to campus users.

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McGreevy, Michael. "Statistical language modelling for large vocabulary speech recognition." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16444/1/Michael_McGreevy_Thesis.pdf.

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The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands on language models. In a large vocabulary system, acoustic confusion is greater, thus there is more reliance placed on the language model for disambiguation. In addition to this, ASR systems are increasingly being deployed in situations where the speaker is not conscious of their interaction with the system, such as in recorded meetings and surveillance scenarios. This results in more natural speech, which contains many false starts and disfluencies. In this thesis we investigate a novel ap

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McGreevy, Michael. "Statistical language modelling for large vocabulary speech recognition." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16444/.

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The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands on language models. In a large vocabulary system, acoustic confusion is greater, thus there is more reliance placed on the language model for disambiguation. In addition to this, ASR systems are increasingly being deployed in situations where the speaker is not conscious of their interaction with the system, such as in recorded meetings and surveillance scenarios. This results in more natural speech, which contains many false starts and disfluencies. In this thesis we investigate a novel approa

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Tan, Ming. "A Large Scale Distributed Syntactic, Semantic and Lexical Language Model for Machine Translation." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1386111950.

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Susman, Derya. "Turkish Large Vocabulary Continuous Speech Recognition By Using Limited Audio Corpus." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614207/index.pdf.

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Speech recognition in Turkish Language is a challenging problem in several perspectives. Most of the challenges are related to the morphological structure of the language. Since Turkish is an agglutinative language, it is possible to generate many words from a single stem by using suffixes. This characteristic of the language increases the out-of-vocabulary (OOV) words, which degrade the performance of a speech recognizer dramatically. Also, Turkish language allows words to be ordered in a free manner, which makes it difficult to generate robust language models. In this thesis, the existing m

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Comez, Murat Ali. "Large Vocabulary Continuous Speech Recogniton For Turkish Using Htk." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1205491/index.pdf.

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This study aims to build a new language model that can be used in a Turkish large vocabulary continuous speech recognition system. Turkish is a very productive language in terms of word forms because of its agglutinative nature. For such languages like Turkish, the vocabulary size is far from being acceptable. From only one simple stem, thousands of new word forms can be generated using inflectional or derivational suffixes. In this thesis, words are parsed into their stems and endings. One ending includes the suffixes attached to the associated root. Then the search network based on bigrams i

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Sagen, Markus. "Large-Context Question Answering with Cross-Lingual Transfer." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-440704.

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Models based around the transformer architecture have become one of the most prominent for solving a multitude of natural language processing (NLP)tasks since its introduction in 2017. However, much research related to the transformer model has focused primarily on achieving high performance and many problems remain unsolved. Two of the most prominent currently are the lack of high performing non-English pre-trained models, and the limited number of words most trained models can incorporate for their context. Solving these problems would make NLP models more suitable for real-world application

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Pontes, Miranda James William. "Federation of heterogeneous models with machine learning-assisted model views." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2025. http://www.theses.fr/2025IMTA0454.

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L’Ingénierie Dirigée par les Modèles (IDM) promeut les modèles comme un élément clé pour répondre à la complexité croissante du cycle de vie des systèmes logiciel. L’ingénierie de systèmes avec l’IDM implique divers modèles représentant différentes aspects du système. Cette hétérogénéité nécessite des capacités de fédération de modèles pour intégrer des points de vue spécifiques à de multiples domaines. Les solutions de Vues sur les Modèles (Model Views) répondent à ce défi mais manquent encore de support à l’automatisation. Cette thèse explore l’intégration de l’Apprentissage Automatique (AA)

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Uzelac, Lawrence Stevan. "A Multiple Coupled Microstrip Transmission Line Model for High-Speed VLSI Interconnect Simulation." PDXScholar, 1991. https://pdxscholar.library.pdx.edu/open_access_etds/4526.

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A model is presented which incorporates the advantages of a mixed mode simulation to characterize transmission line behavior in multiple coupled Transmission line systems. The model is intended for use by digital circuit designers who wish to be able to obtain accurate transmission line behavior for complex digital systems for which continuous time simulation tools such as SPICE would time prohibitive. The model uses a transverse electromagnetic wave approximation to obtain solutions to the basic transmission line equations. A modal analysis technique is used to solve for the attenuation and p

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Books on the topic "Large language model"

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Satō, Hideto. A data model, knowledge base, and natural language processing for sharing a large statistical database. Institute of Social and Economic Research, Osaka University, 1989.

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Amaratunga, Thimira. Understanding Large Language Models. Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7.

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Martra, Pere. Large Language Models Projects. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0515-8.

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Kucharavy, Andrei, Octave Plancherel, Valentin Mulder, Alain Mermoud, and Vincent Lenders, eds. Large Language Models in Cybersecurity. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7.

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Marcondes, Francisco S., Adelino Gala, Renata Magalhães, Fernando Perez de Britto, Dalila Durães, and Paulo Novais. Natural Language Analytics with Generative Large-Language Models. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-76631-2.

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Kamath, Uday, Kevin Keenan, Garrett Somers, and Sarah Sorenson. Large Language Models: A Deep Dive. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65647-7.

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Singh, Bhawna. Building Applications with Large Language Models. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0569-1.

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Grigorov, Dilyan. Introduction to Python and Large Language Models. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0540-0.

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Ganguly, Arindam. Scaling Enterprise Solutions with Large Language Models. Apress, 2025. https://doi.org/10.1007/979-8-8688-1154-8.

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Rane, Nitin Liladhar, Suraj Kumar Mallick, Jayesh Rane, and Chaitanya Baliram Pande, eds. Large Language Models for Sustainable Urban Development. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86039-3.

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Book chapters on the topic "Large language model"

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Gupta, Nikhil, and Jason Yip. "Large Language Model Operations." In Databricks Data Intelligence Platform. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0444-1_11.

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Ren, Jianfeng, and Andric Li. "Large Language Model System Design." In Silicon Valley Python Engineer Interview Guide. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3201-5_24.

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Gutschmidt, Anne, and Benjamin Nast. "Assessing Model Quality Using Large Language Models." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77908-4_7.

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Aneja, Avi, Anuradha Dhull, Akansha Singh, and Krishna Kant Singh. "Large Language Model on Multi-Modal Data." In Multimodal Generative AI. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2355-6_3.

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Wu, Yonghui. "Large Language Model and Text Generation." In Cognitive Informatics in Biomedicine and Healthcare. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55865-8_10.

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Chouhan, Sanjay, Shubha Brata Nath, and Aparajita Dutta. "HindiLLM: Large Language Model for Hindi." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78172-8_17.

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Aavantikal, Rakesh Kumar Dwivedi, and Vivek Kumar. "Text classification using large language model." In Advances in Science, Engineering and Technology. CRC Press, 2025. https://doi.org/10.1201/9781003641544-42.

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Yue, Linan, Qi Liu, Yichao Du, Weibo Gao, Ye Liu, and Fangzhou Yao. "FedJudge: Federated Legal Large Language Model." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-5569-1_17.

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Vishwas, Banglore Vijay Kumar, and Sri Ram Macharla. "Time-LLM: Reprogramming Large Language Model." In Time Series Forecasting Using Generative AI. Apress, 2025. https://doi.org/10.1007/979-8-8688-1276-7_4.

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Li, Xiangyu, Lei Wu, Yu Shang, and Danyang Liu. "Anthropomorphic Enhancement Model Based on Large Language Models." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4016-4_10.

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Conference papers on the topic "Large language model"

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Bishop, Cameron, Xiaodan Zhu, and Karen Rudie. "Large Language Model Translation of Indigenous Languages." In 2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2024. http://dx.doi.org/10.1109/ccece59415.2024.10667295.

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Chen, Chi, Yiyang Du, Zheng Fang, et al. "Model Composition for Multimodal Large Language Models." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.606.

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Chang, Edward Y. "Behavioral Emotion Analysis Model for Large Language Models." In 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2024. http://dx.doi.org/10.1109/mipr62202.2024.00094.

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Chen, Boqi. "Consistent Graph Model Generation with Large Language Models." In 2025 IEEE/ACM 47th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). IEEE, 2025. https://doi.org/10.1109/icse-companion66252.2025.00067.

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Pan, Zibin, Shuwen Zhang, Yuesheng Zheng, Chi Li, Yuheng Cheng, and Junhua Zhao. "Multi-Objective Large Language Model Unlearning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889776.

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Bae, Heui Yeon, Morteza Saberi, Sahar Shariflou, et al. "Enhancing Glaucoma Diagnosis through Vision-Language Models and Large Language Model Descriptions." In 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2024. https://doi.org/10.1109/dicta63115.2024.00039.

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Brach, William, Kristián Košt’ál, and Michal Ries. "Can Large Language Model Detect Plagiarism in Source Code?" In 2024 2nd International Conference on Foundation and Large Language Models (FLLM). IEEE, 2024. https://doi.org/10.1109/fllm63129.2024.10852497.

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Furman, Deborah Ann, Eitan Farchi, Michael Edward Gildein, Andrew C. M. Hicks, and Ryan Thomas Rawlins. "Combinatorial Test Design Model Creation using Large Language Models." In 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2025. https://doi.org/10.1109/icstw64639.2025.10962528.

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Chao, Wen-Shuo, Zhi Zheng, Hengshu Zhu, and Hao Liu. "Make Large Language Model a Better Ranker." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.51.

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Zhang, Mengqi, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, and Zhumin Chen. "Knowledge Graph Enhanced Large Language Model Editing." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.1261.

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Reports on the topic "Large language model"

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Seymore, Kristie, and Ronald Rosenfeld. Large-Scale Topic Detection and Language Model Adaptation. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada327553.

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Zhang, Hao. Large Language Model (LLM) Monthly Report (2024 Apr). ResearchHub Technologies, Inc., 2024. http://dx.doi.org/10.55277/researchhub.0ps6xenm.

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Sun, Ruiqi, and Daniel Trefler. The Impact of AI and Cross-Border Data Regulation on International Trade in Digital Services: A Large Language Model. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w31925.

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Rosenblat, Sruly, Tim O'Reilly, and Ilan Strauss. Beyond Public Access in LLM Pre-Training Data: Non-public book content in OpenAI’s Models. AI Disclosures Project, Social Science Research Council, 2025. https://doi.org/10.35650/aidp.4111.d.2025.

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Using a legally obtained dataset of 34 copyrighted O’Reilly Media books, we apply the DE-COP membership inference attack method to investigate whether OpenAI’s large language models were trained on copyrighted content without consent. Our AUROC scores show that GPT-4o, OpenAI’s more recent and capable model, demonstrates strong recognition of paywalled O’Reilly book content (AUROC = 82%), compared to OpenAI’s earlier model GPT-3.5 Turbo. In contrast, GPT-3.5 Turbo shows greater relative recognition of publicly accessible O’Reilly book samples. GPT-4o Mini, as a much smaller model, shows no kno

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Lavadenz, Magaly, Sheila Cassidy, Elvira G. Armas, Rachel Salivar, Grecya V. Lopez, and Amanda A. Ross. Sobrato Early Academic Language (SEAL) Model: Final Report of Findings from a Four-Year Study. Center for Equity for English Learners, Loyola Marymount University, 2020. http://dx.doi.org/10.15365/ceel.seal2020.

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The Sobrato Early Academic Language (SEAL) Model Research and Evaluation Final Report is comprised of three sets of studies that took place between 2015 and 2019 to examine the effectiveness of the SEAL Model in 67 schools within 12 districts across the state of California. Over a decade ago, the Sobrato Family Foundation responded to the enduring opportunity gaps and low academic outcomes for the state’s 1.2 million English Learners by investing in the design of the SEAL Model. The SEAL PreK–Grade 3 Model was created as a whole-school initiative to develop students’ language, literacy, and ac

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Tranchero, Matteo, Cecil-Francis Brenninkmeijer, Arul Murugan, and Abhishek Nagaraj. Theorizing with Large Language Models. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w33033.

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Ludwig, Jens, Sendhil Mullainathan, and Ashesh Rambachan. Large Language Models: An Applied Econometric Framework. National Bureau of Economic Research, 2025. https://doi.org/10.3386/w33344.

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Humlum, Anders, and Emilie Vestergaard. Large Language Models, Small Labor Market Effects. National Bureau of Economic Research, 2025. https://doi.org/10.3386/w33777.

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Prasad, Jayanti. Large Language Models: AI Foundations and Applications in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/85rfezw01y0q9521.

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This 5-day workshop provides a comprehensive understanding of large language models, their AI foundations, and applications in Python. Designed for PhD students, professors, and professional researchers, the seminar offers hands-on coding sessions, case studies, and discussions on the future of large language models in academic research.

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Link, Hamilton, Brandon Berman, Ryan Cooper, Haley Dodge, Nichole Fluke, and Robert VanCleave. Predictive Indicators of the Performance of Large Language Models. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2480207.

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